Estimation of the Average Kappa Coefficient of a Binary Diagnostic Test in the Presence of Partial Verification

The average kappa coefficient of a binary diagnostic test is a measure of the beyond-chance average agreement between the binary diagnostic test and the gold standard, and it depends on the sensitivity and specificity of the diagnostic test and on disease prevalence. In this manuscript the estimation of the average kappa coefficient of a diagnostic test in the presence of verification bias is studied. Confidence intervals for the average kappa coefficient are studied applying the methods of maximum likelihood and multiple imputation by chained equations. Simulation experiments have been carried out to study the asymptotic behaviors of the proposed intervals, given some application rules. The results obtained in our simulation experiments have shown that the multiple imputation by chained equations method provides better results than the maximum likelihood method. A function has been written in R to estimate the average kappa coefficient by applying multiple imputation. The results have been applied to the diagnosis of liver disease.

Automated summary

This study investigated the estimation of the average kappa coefficient of a diagnostic test in the presence of verification bias, comparing the methods of maximum likelihood and multiple imputation. Simulation experiments showed that multiple imputation by chained equations method yielded better results than the maximum likelihood method, with implications for the diagnosis of liver disease.